3 Sequencing data

3.1 Sequencing depth

tibble(metric=c("Total GB", "Total reads", "Average GB", "Average reads"),
       value=unlist(c(round(all_data %>% summarise(sum(bases_pre_fastp)) / 1000000000,2),
               round(all_data %>% summarise(sum(bases_pre_fastp)) / 300,2),
               paste0(round(all_data %>% summarise(mean(bases_pre_fastp)) / 1000000000,2),"±",round(all_data %>% summarise(sd(bases_pre_fastp)) / 1000000000,2)),
               paste0(round(all_data %>% summarise(mean(bases_pre_fastp)) / 300,0),"±",round(all_data %>% summarise(sd(bases_pre_fastp)) / 300,0))))
       ) %>%
  tt()
tinytable_gj1kkkrl31rmub4op5am
metric value
Total GB 937.37
Total reads 3124578099
Average GB 5.18±2.46
Average reads 17262862±8195554
all_data %>%
    group_by(Taxon,Extraction) %>%
    summarise(value = sprintf("%.1f±%.1f", mean(bases_post_fastp / 1000000000), sd(bases_post_fastp / 1000000000))) %>%
    pivot_wider(names_from = Extraction, values_from = value) %>%
    tt(caption = "Mean and standard deviation of sequencing depth (GB)")
tinytable_fj8yqj4lzdkg88gn7stb
Mean and standard deviation of sequencing depth (GB)
Taxon DREX EHEX ZYMO
Amphibian 3.2±2.1 4.7±0.3 4.0±1.6
Bird 4.2±2.4 3.1±1.9 3.9±1.8
Control 0.5±0.6 2.1±2.7 0.0±0.0
Mammal 4.6±2.0 3.8±2.2 5.4±3.2
Reptile 5.7±1.3 5.0±1.8 6.1±2.2
all_data %>%
    ggplot(aes(x=Extraction,y=bases_pre_fastp))+ 
        geom_boxplot() + 
        facet_grid(. ~ Taxon, scales = "free") +
        labs(y="DNA yield (ng)",x="Extraction method")

all_data  %>%
    filter(Taxon != "Control") %>%
    lmerTest::lmer(bases_post_fastp ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
    broom.mixed::tidy() %>%
    tt()
tinytable_1guxqguef86vgxs0wf07
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 4322719741 382290022 11.30743545 1.711860e+01 2.299227e-09
fixed NA ExtractionEHEX 10864597 284400639 0.03820173 1.443429e+09 9.695268e-01
fixed NA ExtractionZYMO 485437083 283030980 1.71513762 4.313942e+21 8.632000e-02
ran_pars Sample sd__(Intercept) 1407267694 NA NA NA NA
ran_pars Species sd__(Intercept) 508228833 NA NA NA NA
ran_pars Residual sd__Observation 1524168473 NA NA NA NA

3.2 Quality-filtering

all_data %>%
    mutate(qf_bases=bases_post_fastp/bases_pre_fastp*100) %>%
    group_by(Taxon,Extraction) %>%
    summarise(value = sprintf("%.1f±%.1f", mean(qf_bases), sd(qf_bases))) %>%
    pivot_wider(names_from = Extraction, values_from = value) %>%
    tt(caption = "Mean and standard deviation of quality-filtered proportion of reads")
tinytable_y3sdk8zxyvk07ta0lav9
Mean and standard deviation of quality-filtered proportion of reads
Taxon DREX EHEX ZYMO
Amphibian 91.7±3.7 87.6±2.9 84.7±1.2
Bird 70.3±23.0 70.2±14.7 70.9±16.1
Control 9.8±11.5 27.5±3.4 3.3±2.3
Mammal 89.5±4.7 91.2±1.9 91.9±2.3
Reptile 90.5±7.1 88.3±7.2 89.9±6.3
all_data %>%
    mutate(qf_bases=bases_post_fastp/bases_pre_fastp*100) %>%
    ggplot(aes(x=Extraction,y=qf_bases))+ 
        geom_boxplot() + 
        facet_grid(. ~ Taxon, scales = "free") +
        labs(y="DNA yield (ng)",x="Extraction method")

all_data  %>%
    mutate(qf_bases=bases_post_fastp/bases_pre_fastp*100) %>%
    filter(Taxon != "Control") %>%
    lmerTest::lmer(qf_bases ~ Extraction + (1 | Sample) + (1 | Species), data = ., REML = FALSE) %>%
    broom.mixed::tidy() %>%
    tt()
tinytable_kdhhaj4s699vcsx9ljs5
effect group term estimate std.error statistic df p.value
fixed NA (Intercept) 84.867885 3.229677 26.2775145 12.92142 1.337096e-12
fixed NA ExtractionEHEX -1.238440 1.268390 -0.9763877 145.71392 3.304905e-01
fixed NA ExtractionZYMO -1.811218 1.262245 -1.4349184 145.69071 1.534536e-01
ran_pars Sample sd__(Intercept) 7.418801 NA NA NA NA
ran_pars Species sd__(Intercept) 9.356829 NA NA NA NA
ran_pars Residual sd__Observation 6.797395 NA NA NA NA